总结 matplotlib.pyplot.colors()是 matplotlib 库中用于管理颜色的模块,提供了许多颜色表、颜色映射等功能,是数据可视化的重要组成部分。在使用时,我们可以根据具体需要选择特定的颜色表、颜色映射,并使用ScalarMappable等类来实现数据值到颜色的映射。
'''fromcmoceanimportcm# Loop through all methods in cmocean.forname, cmapinvars(cm).items():# See if it is a colormap.ifisinstance(cmap, matplotlib.colors.LinearSegmentedColormap): print(name) x = np.linspace(0,10) X, _ = np.meshgrid(x, x) plt.figure() plt.pcolor(X, cmap=cmap)...
plt.scatter(x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, edgecolors=None, *, data=None, **kwargs) x,y:表示的是大小为(n,)的数组,也就是我们即将绘制散点图的数据点 s:是一个实数或者是一个数组大小为(n,...
cm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF']) cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b']) plt.xlim(x1_min, x1_max) plt.ylim(x2_min, x2_max) plt.pcolormesh(x1, x2, y_predict.reshape(x1.shape), cmap=cm_light) plt.scatter(x_...
pyplot.imshow函数用于显示图像,同时可以添加颜色栏(colorbar)来表示图像的像素值与颜色之间的对应关系。然而,在某些情况下,imshow函数显示的图像与其颜色栏可能不匹配,可能出现颜色栏显示不准确或者不完整的情况。 造成这种不匹配的原因可能有以下几种: 数据范围不匹配:imshow函数默认将图像的最小值映射为颜色栏的最...
Colors in Matplotlib There are many colormaps you can use to map data onto color values. Below we list several ways in which color can be utilized in Matplotlib. For a more in-depth look at colormaps, see theColormaps in Matplotlibtutorial. ...
contour_handles = list()forindex, labelinenumerate(self.unique_labels_display): binary_slice_seg = slice_seg == indexifnotbinary_slice_seg.any():continuectr_h = plt.contour(binary_slice_seg, levels=[cfg.contour_level, ], colors=(self.color_for_label[index],), ...
colors =["green", "orange", "gold", "blue", "k", "#550011", "purple", "red"] axes1.set_title(" color list") contour = axes1.contourf(A, B, X, colors = colors) axes2.set_title("with colormap") cmap = matplotlib.colors.ListedColormap(colors) ...
c:array-like or list of colors or color, optional标记颜色。 import numpy as np import matplotlib.pyplot as plt #创建数据的位置坐标,这里创建了50个数据 X, Y = np.round(np.random.uniform(1, 11, size=50), 2), np.round(np.random.uniform(1, 11, size=50), 2) ...
(int)colors = ['red', 'blue']plt.figure(figsize=(6, 6))for i in np.unique(y):plt.scatter(X[y==i, 0], X[y==i, 1], label = "y="+str(i),color=colors[i], edgecolor="white", s=50)circle = plt.Circle((0, 0), 3.5, color='black', fill=False,linestyle="--", ...